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The characterization of nanostructured surfaces with sensitivity in thesub-nm range is of high importance for the development of current and nextgeneration integrated electronic circuits. Modern transistor architectures fore.g. FinFETs are realized by lithographic fabrication of complex, well orderednanostructures. Recently, a novel characterization technique based on X-rayfluorescence measurements in grazing incidence geometry has been proposed forsuch applications. This technique uses the X-ray standing wave field, arisingfrom an interference between incident and the reflected radiation, as ananoscale sensor for the dimensional and compositional parameters of thenanostructure. The element sensitivity of the X-ray fluorescence techniqueallows for a reconstruction of the spatial element distribution using afinite-element method. Due to a high computational time, intelligentoptimization methods employing machine learning algorithms are essential for atimely provision of results. Here, a sampling of the probability distributionsby Bayesian optimization is not only fast, it also provides an initial estimateof the parameter uncertainties and sensitivities. The high sensitivity of themethod requires a precise knowledge of the material parameters in the modelingof the dimensional shape provided that some physical properties of the materialare known or determined beforehand. The unknown optical constants wereextracted from an unstructured but otherwise identical layer system by means ofsoft X-ray reflectometry. The spatial distribution profiles of the differentelements contained in the grating structure were compared to scanning electronand atomic force microscopy and the influence of carbon surface contaminationon the modeling results were discussed.

Shape- and element-sensitive reconstruction of periodic nanostructures with grazing incidence X-ray fluorescence analysis and machine learning